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[Artificial Intelligence and Cerebellar Motor Learning].

Soichi Nagao1, Takeru Honda

  • 1Nozomi Hospital, Laboratory for Integrative Brain Function.

Brain and Nerve = Shinkei Kenkyu No Shinpo
|July 11, 2019
PubMed
Summary

The cerebellum

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Artificial Intelligence

Background:

  • Cerebellar learning models trace back to Marr and Albus's perceptron models.
  • The Marr-Albus-Ito hypothesis integrated Ito's flocculus hypothesis and Purkinje cell long-term depression.
  • The liquid-state machine (LSM) model expanded perceptron models with recurrent neural networks.

Purpose of the Study:

  • To review the historical development of cerebellar learning models.
  • To highlight the liquid-state machine (LSM) model's contributions to understanding cerebellar function.
  • To explore the cerebellum's role as a potential origin of artificial intelligence (AI).

Main Methods:

  • Historical review of cerebellar learning models.
  • Description of the liquid-state machine (LSM) model and its extensions.

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  • Conceptual comparison of the LSM model with deep learning.

    • Main Results:

    • Cerebellar learning models evolved from simple perceptrons to complex networks like the LSM.
    • The LSM model successfully explains various cerebellar functions, including motor learning and memory.
    • The cerebellum's computational principles are seen as foundational to modern AI.

    Conclusions:

    • The cerebellum is a crucial area for understanding learning and memory.
    • The LSM model provides a framework for AI development inspired by the brain.
    • The evolution of AI has significant implications for clinical cerebellar neurology.